1. Field of the Invention
The invention is related to signal/image processing and encoding. More specifically, the invention relates to architectures to execute signal/image processing functions.
2. Description of Related Art
Using traditional Fourier analysis (transforms), any signal can be approximated as a sum of sinusoidal waveforms of assorted frequencies. While Fourier transforms are ideally suited for to signals having repeated behavior, such as in speech signals, it fails to efficiently approximate signals with sharp discontinuities such as the edge features in images, or signals encoded for digital communications. Therefore, in recent years, a new form of analysis, known as Wavelet analysis has been developed to better represent signals that have exaggerated and discontinuous features. A transform, similar to the Fourier transform, Discrete Wavelet Transform (DWT), based on Wavelet analysis, has been developed to represent signals with discontinuous features. The DWT may be a "discrete" algorithm, which indicates that rather than approximating a signal using continuous waveforms, the signal is approximated by discrete samples of waveform. Since the transform is discrete, the DWT can be implemented using digital logic such as Very Large Scale Integrated (VLSI) circuits and thus can be integrated on a chip with other digital components.
The essence of DWT is to decompose an input signal into two or more frequency sub-bands. An input signal may be decomposed into two outputs--a low frequency sub-band output obtained using a low-pass filter, and a high frequency sub-band output using a high-pass filter. Each of these sub-bands can be encoded separately using a suitable coding system. Each sub-band can further be divided into smaller and smaller sub-bands as is required. If an input signal is decomposed into two sub-bands, then to reconstruct the input signal, the VLSI architecture used must be able to receive two inputs and return one output. Fundamentally, therefore, the forward DWT transform, i.e., the transform performing the decomposition, is asymmetric to the inverse DWT transform, i.e., the transform performing the reconstruction since they require different numbers of inputs and an outputs. Thus, traditional VLSI architectures for the DWT computation (decomposition and reconstruction) have two separate and distinct circuitry, one for the forward DWT and one for inverse DWT. The circuitry of such architecture is complicated further since the forward and inverse transforms use different filter coefficients and schedule (delay) certain inputs in differing stages of the computation.
To reduce the speed and cost of the forward DWT and inverse DWT transform, therefore, there is needed a single integrated architecture which can perform both the forward DWT transform and the inverse DWT transform without separate circuitry or processing elements. Further, the separate architectures for the forward DWT and inverse DWT if needed must also be reduced in complexity.